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hugging-quants/
Meta-Llama-3.1-405B-Instruct-BNB-NF4

Name: Meta-Llama-3.1-405B-Instruct-BNB-NF4
Availability: InStock

Model Information

The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.

This repository contains meta-llama/Meta-Llama-3.1-405B-Instruct quantized using bitsandbytes from BF16 down to NF4 with a block size of 64.

Model Usage

[!NOTE] In order to run the inference with Llama 3.1 405B Instruct BNB in NF4, around 220 GiB of VRAM are needed only for loading the model checkpoint, without including the KV cache or the CUDA graphs, meaning that there should be a bit over that VRAM available.

In order to use the current quantized model, support is offered for different solutions as transformers, or text-generation-inference.

🤗 transformers

In order to run the inference with Llama 3.1 405B Instruct BNB in NF4, both torch and bitsandbytes need to be installed as:

pip install "torch>=2.0.0" bitsandbytes --upgrade

Then, the latest version of transformers need to be installed, being 4.43.0 or higher, as:

pip install "transformers[accelerate]>=4.43.0" --upgrade

To run the inference on top of Llama 3.1 405B Instruct BNB in NF4 precision, the model can be instantiated as any other causal language modeling model via AutoModelForCausalLM and run the inference normally.

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "hugging-quants/Meta-Llama-3.1-405B-Instruct-BNB-NF4"
prompt = [
  {"role": "system", "content": "You are a helpful assistant, that responds as a pirate."},
  {"role": "user", "content": "What's Deep Learning?"},
]

tokenizer = AutoTokenizer.from_pretrained(model_id)

inputs = tokenizer.apply_chat_template(prompt, tokenize=True, add_generation_prompt=True, return_tensors="pt").cuda()

model = AutoModelForCausalLM.from_pretrained(
  model_id,
  torch_dtype=torch.bfloat16,
  low_cpu_mem_usage=True,
  device_map="auto",
)

outputs = model.generate(inputs, do_sample=True, max_new_tokens=256)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))